Adatmenedzsment Microservices Architecture, Oracle Database, OCI Fekete Zoltán Principal Solution Engineer 1 Copyright © 2019 Oracle and/or its affiliates. Safe Harbor The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation. Statements in this presentation relating to Oracle’s future plans, expectations, beliefs, intentions and prospects are “forward-looking statements” and are subject to material risks and uncertainties. A detailed discussion of these factors and other risks that affect our business is contained in Oracle’s Securities and Exchange Commission (SEC) filings, including our most recent reports on Form 10-K and Form 10-Q under the heading “Risk Factors.” These filings are available on the SEC’s website or on Oracle’s website at http://www.oracle.com/investor. All information in this presentation is current as of September 2019 and Oracle undertakes no duty to update any statement in light of new information or future events. Copyright © 2019 Oracle and/or its affiliates. Transforming Monoliths into Microservices Copyright © 2019 Oracle and/or its affiliates. I.T Cost and Complexity • For decades, I.T. has been costly and slow moving • This presentation will focus on Data Management • Root cause is complexity caused by: • Enterprise Product Complexity • Systems Integration Complexity • New technologies can finally eliminate the sources of I.T. complexity 4 Copyright © 2020, Oracle and/or its affiliates. All rights reserved. Komplexitás és költség • IT költségek és sok idő • A fő ok a komplexitás, sok tevékenység, menedzsment • Új megközelítések eltüntetik a komplexitás okait • Autonomous management, cloud, Machine Learning • Konvergens megoldás a szükséges funkciókkal a rendszerintegráció komplexitását eliminálja Data Strategy • Modern Applications require many different: • Data Types - Relational, Document, Spatial, Graph, etc. • Workloads - Transactions, analytics, ML, IoT, etc. • Each data type and workload requires different database algorithms • Two possible Data Strategies: • Use single-purpose “best-of-breed” database for each data type and workload • Use a converged database for all data types and workloads 6 Copyright © 2020, Oracle and/or its affiliates. All rights reserved. 7 Why Microservices? • Develop application as suite of loosely-coupled small services, each running in its own context and communicating with lightweight mechanisms • Enables rapid, frequent and reliable delivery of complex applications • Each microservice should be • Highly maintainable and testable • Loosely coupled • Horizontally scalable • Independently deployable with own database • Organized around business capabilities • Owned by a small team “Polyglot persistence will occur over the enterprise as different applications use different data storage technologies. It will also occur within a single application as different parts of an application’s data store have different access characteristics.” Martin Fowler & Pramod Sadalage, Feb. 2012 http://martinfowler.com/articles/nosql-intro-original.pdf Source: The future is: NoSQL Databases Polyglot Persistence http://martinfowler.com/articles/nosql-intro-original.pdf Different apps/ µServices have different needs Financial Recommend. Product User Sessions Shopping Cart Reporting Analytics Activity Logs Transactions Engine Catalog Heavy Writes √ Heavy Reads √ √ √ √ Fast Read/Write √ Data Consistency √ Data Durability √ Analytic √ √ Processing Graph √ Spatial Geo Distribution √ √ √ Relational √ √ √ Key/Value √ √ √ Data Document/JSON √ √ Graph √ Real-World Example of Macro-Complexity Producers Event and data streams Consumers Macro-Complexity Pub/Sub Web servers Mobile App1 • Multiple technologies Order service Mobile Platform Search Portal Apache ELK • Multiple data stores API/Brokers Kafka Ops Mobile Apache Flink Dashboards • Data copied multiple IoT Realtime times to do analytics Analytics, Transactions AWS S3 Alerts • Compromises security Oracle ML Model • Compromises data Training consistency NoSQL Hadoop lake Adhoc Exploration • Complex to maintain Analytic MySQL Reports • Need highly skilled developers to build & OLTP Vertica/Hive keep running 11 One Converged Database vs Several Specialized Databases Modern Information Systems Architectural Strategies Data Types AWS Relational, Document, Event, Spatial, Graph Run separate Specialized Databases etc. for each data type Oracle Application Types Run one Converged Database Transactions, Analytics, Microservices, ML, IoT, etc. that supports multiple data types Considerations for Converged Converged approach: • Benefits of consolidation and standardization • Standardized administration • Consistent data security policies • Simple integration across multiple data formats • Transactions and data consistency Single-model Polyglot: • Benefits of specialization • Specialized APIs • Specialized data formats • Specialized access methods and indexes The Hidden Pain of Data Management and µServices • µService philosophy encourages data store independence • Choose the right data store for the characteristics of your service • Data store separation comes with tradeoffs and complexities that multiply with the granularity and interdependence of µServices • Data Consistency: Important data elements across µServices should have the same format, meaning, and ultimately values • Data Sharing: Do you replicate or aggregate common data with µServices? Are you building 2PC across µServices or creating an ETL headache? • Data Security/Governance: Are you propagating sensitive data, or creating a massive threat surface? • Overall complexity: As each µService adds its own unique technology stack it increases the operational overhead for the company overall. Fragmentated Data Architecture Creates Complexity Functional Isolation Leads to Complexity • Each single-purpose database that is deployed fragments the data architecture • Different proprietary APIs, languages, and transaction models • Different operational needs and limitations • Must continually transform data and propagate changes – causing data delays and data divergence • Must separately implement HA and Security policies in every database to accommodate their differences • End-to-end application security, availability, scalability, consistency, etc. limited by the weakest of the databases • Intent was “best-of-breed”, result is “worst-of-weakness” 15 Copyright © 2019, Oracle and/or its affiliates. All rights reserved. Fragmented Features vs. Converged Product • Phone calls, messaging, camera, calendar, etc. used to require separate products • Now converged into features of smartphones • Similarly, key-value, analytics, JSON, sharding, etc. originally required separate products • Now converged into features of Converged Database • Simpler, better, and creates powerful synergies across features 16 Copyright © 2019, Oracle and/or its affiliates. All rights reserved. µService Data management Tradeoffs Separate 1 DMP 1 DMP 1 DMP Tradeoffs DMPs Single Schema Multi Schema PDB per Service Dev Agility Dev Choice of data model/structure Service Isolation Data Consistency Data Data Sharing Data Security Common Security Model OPS Independent Service Scaling Common Management and HA DMP = Data Management Platform Over Time New Functionality is Converged Into Mainstream • Single-purpose databases have emerged many times • Abandoned after features are added to converged databases Cobol ISAM Object DBs XML DBs JSON DBs Relational Next DB DB Converged DB Converged Converged Solution Complexity Solution DB DB 1970s 1980s 1990s 2000s 2010s 20?? 18 Copyright © 2020, Oracle and/or its affiliates. All rights reserved. Oracle Autonomous Database Converged Features Multi: tenant, language, model Multitenant In-Memory Hyperscale Analytics Multitenant for Efficient, Agile Database Clouds In-Memory for Database Acceleration { } Sharding for Hyperscale and Geo Distribution Native JSON for Document Data JSON In-Memory Cloud IoT Integration In-Memory Ingest for Fastest IoT Cloud SQL for integrating Object Store Data Lake AutoML for simple integrated Machine Learning Blockchain Persistent Machine Persistent Memory Store for Lowest Latency Memory Learning Blockchain Tables for Preventing Fraud Spatial and Graph for Mapping and Social Networks Events for Transactional Event-driven Microservices Spatial Graph Events And many more … https://blogs.oracle.com/database/what-is-a-converged-database Polyglot Persistence Market Trends • Single-model architectures are most pervasive for ‘edge’ applications • New business & workload requirements • Business applications naturally converge to multi-model architectures • Today’s ‘edge’ applications are tomorrow’s mainstream business applications • Efficiencies of multi-model architecture override advantages of special- purpose systems over time • There will always be single-model polyglot architectures • Because there are always new ‘edge’ applications • Oracle’s single-model architectures: • Oracle Berkeley DB, Oracle NoSQL Database, Essbase, Oracle Big Data Spatial and Graph What Customers Have Asked For • Microservices support Producers Converged database Consumers + • Cool